Unlocking ground-based imagery for habitat mapping

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Unlocking ground-based imagery for habitat mapping. / Morueta-Holme, N.; Iversen, L. L.; Corcoran, D.; Rahbek, C.; Normand, S.

In: Trends in Ecology & Evolution, Vol. 39, No. 4, 2024, p. 349-358.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Morueta-Holme, N, Iversen, LL, Corcoran, D, Rahbek, C & Normand, S 2024, 'Unlocking ground-based imagery for habitat mapping', Trends in Ecology & Evolution, vol. 39, no. 4, pp. 349-358. https://doi.org/10.1016/j.tree.2023.11.005

APA

Morueta-Holme, N., Iversen, L. L., Corcoran, D., Rahbek, C., & Normand, S. (2024). Unlocking ground-based imagery for habitat mapping. Trends in Ecology & Evolution, 39(4), 349-358. https://doi.org/10.1016/j.tree.2023.11.005

Vancouver

Morueta-Holme N, Iversen LL, Corcoran D, Rahbek C, Normand S. Unlocking ground-based imagery for habitat mapping. Trends in Ecology & Evolution. 2024;39(4):349-358. https://doi.org/10.1016/j.tree.2023.11.005

Author

Morueta-Holme, N. ; Iversen, L. L. ; Corcoran, D. ; Rahbek, C. ; Normand, S. / Unlocking ground-based imagery for habitat mapping. In: Trends in Ecology & Evolution. 2024 ; Vol. 39, No. 4. pp. 349-358.

Bibtex

@article{dfd7b50c07834b65a61a9001b891b663,

title = "Unlocking ground-based imagery for habitat mapping",

abstract = "Fine-grained environmental data across large extents are needed to resolve the processes that impact species communities from local to global scales. Ground-based images (GBIs) have the potential to capture habitat complexity at biologically relevant spatial and temporal resolutions. Moving beyond existing applications of GBIs for species identification and monitoring ecological change from repeat photography, we describe promising approaches to habitat mapping, leveraging multimodal data and computer vision. We illustrate empirically how GBIs can be applied to predict distributions of species at fine scales along Street View routes, or to automatically classify and quantify habitat features. Further, we outline future research avenues using GBIs that can bring a leap forward in analyses for ecology and conservation with this underused resource.",

keywords = "biodiversity, habitat complexity, image recognition, remote sensing, Street View",

author = "N. Morueta-Holme and Iversen, {L. L.} and D. Corcoran and C. Rahbek and S. Normand",

note = "Publisher Copyright: {\textcopyright} 2023 Elsevier Ltd",

year = "2024",

doi = "10.1016/j.tree.2023.11.005",

language = "English",

volume = "39",

pages = "349--358",

journal = "Trends in Ecology & Evolution",

issn = "0169-5347",

publisher = "Elsevier Ltd. * Trends Journals",

number = "4",

}

RIS

TY - JOUR

T1 - Unlocking ground-based imagery for habitat mapping

AU - Morueta-Holme, N.

AU - Iversen, L. L.

AU - Corcoran, D.

AU - Rahbek, C.

AU - Normand, S.

PY - 2024

Y1 - 2024

N2 - Fine-grained environmental data across large extents are needed to resolve the processes that impact species communities from local to global scales. Ground-based images (GBIs) have the potential to capture habitat complexity at biologically relevant spatial and temporal resolutions. Moving beyond existing applications of GBIs for species identification and monitoring ecological change from repeat photography, we describe promising approaches to habitat mapping, leveraging multimodal data and computer vision. We illustrate empirically how GBIs can be applied to predict distributions of species at fine scales along Street View routes, or to automatically classify and quantify habitat features. Further, we outline future research avenues using GBIs that can bring a leap forward in analyses for ecology and conservation with this underused resource.

AB - Fine-grained environmental data across large extents are needed to resolve the processes that impact species communities from local to global scales. Ground-based images (GBIs) have the potential to capture habitat complexity at biologically relevant spatial and temporal resolutions. Moving beyond existing applications of GBIs for species identification and monitoring ecological change from repeat photography, we describe promising approaches to habitat mapping, leveraging multimodal data and computer vision. We illustrate empirically how GBIs can be applied to predict distributions of species at fine scales along Street View routes, or to automatically classify and quantify habitat features. Further, we outline future research avenues using GBIs that can bring a leap forward in analyses for ecology and conservation with this underused resource.

KW - biodiversity

KW - habitat complexity

KW - image recognition

KW - remote sensing

KW - Street View

U2 - 10.1016/j.tree.2023.11.005

DO - 10.1016/j.tree.2023.11.005

M3 - Journal article

C2 - 38087707

AN - SCOPUS:85179818107

VL - 39

SP - 349

EP - 358

JO - Trends in Ecology & Evolution

JF - Trends in Ecology & Evolution

SN - 0169-5347

IS - 4

ER -

ID: 378766323

Globe Institute